Digital Manufacturing Applications Powering Future Enterprise Initiatives
Connectivity, intelligence, and flexible automation are at the core of the sweeping technologies powering the Industry 4.0 revolution. While digital manufacturing has been a key part of this revolution, the time has come to revisit the maturity of its use cases in light of the flurry of new-age technologies that have entered the mainstream.
For example, AR/VR, considered by many to be a fringe technology, suddenly saw its use cases rise exponentially in 2020 across industries such as aerospace, manufacturing & retail.
In a nutshell, the integration of disparate systems and processes with new-age digital solutions has opened up a myriad of benefits for companies in terms of agility, flexibility & operational performance.
Below are a few of these digital manufacturing use cases, their maturity in terms of real-world applications, and how they are transforming the value chain across industries.
Advanced robotics to streamline warehouse operations
DHL’s distribution centers in the Netherlands are piloting the use of Autonomous Mobile Robots (AMRs) for locating, tracking, and moving inventory in warehouse and logistics facilities.
Developed by Fetch Robotics, these AMRs autonomously move alongside the workers, automatically learning and sharing the most efficient travel routes to move consignments across the warehouse.
Such robots that work collaboratively with humans, aka Cobots, help reduce order cycle time by up to 50%!
So yes, the use of advanced robotics has definitely matured in manufacturing.
Consider yet another example: GM’s use of a network of connected robots equipped with predictive analytics features to paint, dispense and weld cars.
Built in collaboration with FANUC & Cisco, the goal of the exercise was to build a zero-downtime factory floor. This was achieved thanks to the built-in ML modules within the army of cloud-connected robots that could accurately predict when a unit was in danger of breaking down. Shift managers could then schedule a maintenance break and remove it temporarily from the assembly line.
Since the program began in 2017, GM estimates that they have avoided over 100 unscheduled maintenance breaks. With a minute of downtime equaling almost $20,000, the benefits are obvious.
Rapid prototyping & mass customization using additive manufacturing
While additive manufacturing, or 3d printing, was once solely considered as the fast way to push out prototypes, today, the technology has evolved to a point where finished products can be built in less than half the time as compared to conventional manufacturing methods.
The availability of design files in virtual inventories to enable on-demand and on-prem manufacturing has given birth to a new era of distributed manufacturing.
Industrial grade, on-demand manufacturing company Fast Radius has created a virtual parts warehouse consisting of 3000 items for heavy equipment manufacturing. This saves them from the cost of storing rarely-ordered, expensive-to-manufacture spare parts. When the order comes through, the design file can be executed through any of its global centers.
Optimizing production & supply chain with big data analytics
Yes, big data & analytics has been the mainstay for a while now, but it is only in the past two years that their application in manufacturing has picked up the pace.
At its Automotive Diesel System factory in China, Bosch has successfully embedded sensors into the factory’s machines that provide real-time updates to workers on bottlenecks and sub-optimal operations.
On the other hand, automotive giant Ford is leveraging big data to gain a 360-degree view of the customer in order to better predict their needs & personal preferences.
The massive volume of supply chain data from embedded IoT services are aiding enterprises to predict and optimize the supply-chain with never-before-seen accuracy. This data is extracted from ERP systems, web logs, GPS, RFID, mobile devices and even through social channels.
Developing & optimizing from a distance with digital twin technology
By 2021, 50% of large industrial companies will use digital win technology to drive the business impact of the IoT economy. This emerging technology offers a powerful way to monitor and control assets and simulate processes virtually.
Unlike traditional engineering simulation, digital twins runs online based on data received from sensors connected to the machine. A key component of Industrial IoT, digital twin systems are capable of maintaining high fidelity with their original counterparts.
Not only can operators identify why a part is malfunctioning, they can also use the technology to predict the lifespan of a part/device.
Advances in cloud computing, machine learning and sensors are expanding their use cases to medical diagnostics, factory maintenance, and smart cities as well.
Auto racing team Team Penske is collaborating with Siemens to develop a digital twin of their racing car. The sensors embedded in the real car collect information pertaining to tire pressure, engine control & wind speed and transfer these data to the virtual car model. Engineers are then able to tweak the virtual cars to optimize the performance without experimenting and potentially breaking the actual physical models.
Maturing use cases validate enterprise investments
It’s not just in traditionally manufacturing-heavy sectors like automotive & consumer electronics. Digital manufacturing practices have taken root in as disparate fields as healthcare, pharma & even retail.
Draup’s sales intelligence indicates that the main reasons enterprises are pivoting to smart digital manufacturing are:
- Rapid deployment
- Low capital expenditure
- Unlimited complexity
- Freedom to redesign
- Short-run manufacturing
- Anywhere/anytime manufacturing dependent on current demand
Our platform tracks real-time industry data to identify viable digital manufacturing use cases that service providers can leverage to win huge deals. The comprehensive data feed also delivers concise insights on market movements and technology trends affecting industry output in the short and long term.